Computer Science and Mathematics

Abstract: Governance research commonly evaluates political systems through regime classifications, institutional procedures, or isolated measures of state capacity. Such approaches provide limited insight into how governance capabilities interact dynamically to influence human flourishing. This conceptual paper develops the General Governance Success Model (GSM), a systems-theoretical metamodel representing governance as a complex adaptive and information-processing system. The model defines governance through five coupled capabilities: accountability, institutional competence, social cohesion, strategic continuity, and adaptive learning. Drawing on cybernetics, the Viable System Model, system dynamics, and behavioural theories of bounded rationality, the framework specifies governance performance as an outcome of capability levels, complementarities, feedback delays, and functional misalignment. Governance capabilities and flourishing outcomes are modelled as co-evolving state vectors subject to institutional, resource, legal, and normative constraints. The paper identifies empirically testable hypotheses concerning capability interactions, misalignment, resilience, and the incremental explanatory value of the model beyond regime classification. It also outlines a comparative validation strategy combining dynamic panel analysis, model comparison, sensitivity analysis, and future agent-based simulation. The framework treats computational methods as decision-support and explanatory tools rather than substitutes for political judgment. Normative objectives and model weights remain subject to public deliberation, constitutional rights, transparency, and contestability. The model contributes a transdisciplinary foundation for analysing how interacting governance capabilities may support sustainable and distribution-sensitive human flourishing.

Abstract: Cyber-Syndrome denotes the physical, social, and thinking disorders arising from problematic cyberspace interactions, with its thinking dimension encompassing cognitive deficits (e.g., attentional impairment, executive dysfunction) and affective disturbances (e.g., mood disorders, emotional dysregulation). Conventional assessment approaches, relying primarily on self-report questionnaires and laboratory-based tasks, are unable to capture the rapid, context-dependent fluctuations of mental states in real-world digital environments. Leveraging advances in computational psychiatry, affective computing, and machine learning, this paper presents a scoping review of computational approaches for inferring cognitive and affective states of Cyber-Syndrome from digital behavior. It situates Cyber-Syndrome within the Cyber-Physical-Social-Thinking (CPST) space framework, reviews the principal data sources, and covers the computational methods used to analyze them. It then synthesizes evidence from adolescent studies and examines the research institutions and open-source platforms that have driven methodological progress in the field. Finally, it discusses key challenges and outlines future directions essential for clinical translation and real-world deployment, particularly in adolescent mental health.

Abstract: Falls represent a critical public health concern, particularly for older adults and individuals with disabilities, often resulting in serious injuries, loss of independence, and increased mortality. Traditional fall detection systems suffer from high false positive rates, privacy concerns, and limited real-world applicability. This study presents an improved non-wearable fall detection method integrating YOLOv8-based skeletal pose estimation with load distribution sensing through decision-level sensor fusion. The system employs YOLOv8 for real-time human posture analysis and skeletal motion-based fall recognition, addressing limitations of simple presence-based or LiDAR-only approaches. A distributed Force-Sensitive Resistor (FSR) array embedded in the sensing surface monitors relative floor-loading patterns and impact-related changes. Decision-level fusion with temporal filtering combines skeletal postural and motion information with floor-load evidence, enabling accurate fall detection with substantially fewer false positives than the vision-only configuration. Key methodologies include detailed load sensor calibration, time-domain filtering to distinguish intentional lying from falls, and fusion logic leveraging the complementary strengths of visual and floor-based FSR sensing. Experimental evaluation in a controlled indoor environment configured to represent a residential setting achieved an F1-score of 0.935, specificity of 97.5%, and an 82% reduction in false-positive count compared with the vision-only configuration. This modular ROS2-based system provides a practical, non-intrusive framework with potential for future deployment in residential and assisted-living settings, while supporting extension to broader assistive monitoring applications.

Abstract: Do ethics enable or hinder mission success and the strategic objectives an organisation aims to achieve? Ethics assurance answers this question by creating the grounds for justified evidence-based confidence that identified and mitigated moral risks enable mission success. We study this question in the context of swarm engineering with a use case where an unmanned aircraft traffic management (UTM) system is used for a swarm of unmanned aerial vehicles (UAVs) to deter a swarm of birds away from airport runways. The use case is chosen to showcase ethics in the aviation ecosystem, where ethics is not limited to humans alone but also extends to the wider ecosystem, including living beings such as birds. We adopt pluralism, where the decision-making process combines normative ethics (deontological, virtue, and consequentialism) and descriptive ethics, balanced by human, animal and legal factors. We propose the novel concept of moral vectoring to show how simulation and optimisation can navigate the scenario space and how agents can navigate situations with moral tensions. In the proposed methodology, optimisation does not replace moral judgement, nor do we accept that moral judgement becomes an optimisation process. Both optimisation and moral judgement are subject to scrutiny in the assurance process. We propose concise definitions to enhance clarity and guide the design of a methodology for ethics assurance. Along the way, we identify fundamental challenges that need to be addressed: structuring the moral space, defining scoring functions, and resolving moral tensions when they arise. We conclude with a discussion of implications and future work.

Abstract: This paper presents findings from a mixed-methods study eliciting requirements for a Future Museum ecosystem connecting tangible artefacts with their historical, social, and cultural contexts through citizen curation and connected heritage experiences, including virtual worlds and game-based experi-ences. Thirty participants watched a concept video, completed semi-structured interviews, played two levels of the Discover Babylon serious game as a technology demonstrator, and completed post-game evaluation and requirements elicitation questions. Participants valued digital artefacts that could be collected, curated, shared and connected to contextual information and experiences. They also wanted virtual museum spaces to retain the architectural character and presentation conventions of physical museums. Connected heritage experiences were expected to place artefacts within historical environments, demonstrate their creation and use, and support exploration, puzzles, quizzes and challenges. Participants identified limitations in Discover Babylon’s dated controls, navigation, pacing and audiovisual presentation, but valued its historical setting, opportunities for exploration, puzzles, educational content and representation of Mesopotamian life. Across the Future Museum concept, col-lection was valued not only as a game-like incentive, but also as a means of personal curation, learning and social sharing. The study contributes requirements for Future Museum ecosystems, connected heritage experiences and modern game-like implementations. Its originality lies in conceptualising the Future Museum not as a single virtual museum or standalone application, but as an interconnected ecosystem linking physical museum contexts, virtual artefacts, museum architecture, personal and shared collections, and heritage experiences to support access, contextual understanding, curation and engagement with less familiar histories and overlooked cultural artefacts. Its significance lies in identifying requirements for connecting these elements to support access, contextual understand-ing, curation, and engagement. Its significance lies in identifying requirements for connecting these elements to support access, contextual understanding, curation and engagement.

Abstract: Agrivoltaics (AV) integrates photovoltaic electricity generation with continued agricultural production on the same land. This critical integrative review evaluates AV as solar-energy infrastructure rather than solely as an agronomic intervention. The review was conducted using explicit search terms, eligibility criteria, and a structured appraisal of study design, reporting completeness, representativeness, and uncertainty. This paper’s contribution is a configuration- and context-sensitive synthesis that connects engineering design, land-use performance, techno-economic viability, policy, and equity within a unified energy-systems framework. Across the retained sources, no configuration is universally superior: elevated and tracking systems preserve machinery access but increase structural costs; spaced arrays reduce PV density; and vertical bifacial systems can improve temporal generation profiles, although performance depends strongly on latitude, row spacing, rear-side irradiance, and seasonal albedo. Land equivalent ratios frequently exceed unity, but inconsistent baselines prevent pooled interpretation. Likewise, reported cost premiums and payback periods are not transferable without specifying system configuration, project scale, crop type, electricity price, financing conditions, and policy support. The review identifies four priority gaps: standardized joint PV–crop testing protocols, long-term matched field data, multi-output economic valuation, and explicit representation of AV archetypes and equity constraints in spatial and capacity-expansion models. Scalable deployment requires configuration-specific engineering, measurable agricultural safeguards, coordinated energy and land-use policy, and ownership structures that retain value within farming communities.

Abstract: An LLM producing the response pattern associated with a human psychological effect is not the same claim as the LLM possessing that bias. We present PsyAgentBench, a benchmark that re-runs classic psychology experiments on LLM agents under a factorial design built to separate these: each paradigm is run with the paradigm explicitly labeled in the prompt (named) or framed as a routine task (blind), and on the literal textbook version of the task (canonical) or a structurally matched variant written to reduce lexical and scenario overlap with likely training data (counterfactual), crossed with a persona manipulation. Across five completed paradigms, evaluated on up to three open-weight model families with 41,904 trials released, apparently human-like effects arise through qualitatively different routes rather than one susceptibility: paradigm-label gating with explicit override (Asch conformity, 0 percent blind to 83.3 percent named on gpt-oss-120B), knowledge-dependent signal reliance (anchoring, exactly zero on grounded facts versus near total on invented quantities, a pattern equally consistent with rational use of the only available signal), amplification on novel content under labeling (framing), robust absence (sunk cost), and safety-mediated selection where refusal itself is the primary finding (minimal-group allocation). A one-sentence persona change (agreeableness, framed as an instruction rather than a verified trait manipulation) eliminates, dampens, or reverses these effects depending on which effect it is, arguing against any single response-bias account. We further formalize, and in two cases document empirically, three ways a psychology paradigm can fail to port to LLM agents: persona dominance, population collapse, and safety selection. We argue scalar bias-susceptibility scores obscure this structure and report replication profiles instead.

Abstract: Legal artificial intelligence is moving beyond answer generation into consequential workflows spanning legal research, drafting, compliance, litigation support, public legal services, and regulated legal practice. This transition exposes the limits of conventional evaluation: a plausible answer may still rely on inappropriate authority, outdated law, an inapplicable jurisdiction, or an unreviewable process. We conduct a structured mid-year review of developments in trustworthy legal reasoning made public between 1 January and 30 June 2026. The study integrates four coded datasets: 141 topic-relevant publications meeting the review’s quality criteria, 99 product launch or major-update records, 53 curated public events, and 69 policy or regulatory records. In the publication sample, legal retrieval or retrieval-augmented generation appeared in 109 papers (77.3%), benchmark and evaluation research in 67 (47.5%), and legal agents or simulation in 30 (21.3%). Retrieval quality was addressed in 107 papers (75.9%), whereas temporal validity appeared in only 18 (12.8%) and uncertainty or refusal in nine (6.4%). Product activity was also geographically concentrated: developers headquartered in the United States and the United Kingdom accounted for 68.7% of observed records, while major updates outnumbered new launches. Across the four datasets, the field is moving toward workflow level, retrieval-grounded, and agentic legal AI, but temporal and jurisdictional validity, actionable uncertainty, reproducible oversight, and contestability remain unevenly operationalised. We argue that trustworthy legal reasoning is not a property of a model alone, but an institutional achievement requiring authoritative sources, inspectable processes, accountable human roles, and effective routes for challenge and correction.

Abstract: Computer models and protocols commonly used for superficial thermotherapy assume anatomically symmetric tissue geometries. However, anatomical variability, particularly the variability of the adipose tissue thickness may have a significant impact on heat transfer and cause non-uniform thermal doses in the patient population. This paper studies the influence of anatomical asymmetry due to adipose tissue thickness on heat propagation during superficial thermotherapy based on combined computational and experimental analyses. A Finite Element Model of the lower limb was designed with anatomically representative layers and tissue thermal properties. Parametric simulations were conducted for medium (M) and extra-large (XL) models at hot-pack temperatures (38–44 °C) for 15 min. The simulation results were verified experimentally on ex vivo porcine tissue. The results showed that the thickness of adipose tissue significantly affected deep-tissue heating. When the same heating condition was applied, the boundary temperature of the muscle was increased by ~1.03 °C for the 44 °C hot-pack in the M-size model and was restricted to 0.21 °C for the XL-size model. The experimental measurements indicated an increase of 1.16 °C in the muscle temperature, with an average difference of 0.81 °C between numerical predictions and experimental measurements. These results show that anatomical asymmetry has a strong effect on heat transfer during superficial thermotherapy and challenge the precept that standard heating protocols provide similar thermal doses in different body morphologies.

Abstract: The need for efficiency in radiology has been made clear and augmented reality (AR) has the potential to address this by aiding with issues around irregular lighting conditions impacting the accuracy of reports, while also speeding up the process with improved interactions. However, some interaction questions are still very much open and interactions for radiological applications deserve more attention. A series of seven interviews with practising radiologists and radiology registrars was conducted where AR tasks with the Microsoft HoloLens 2 and Meta Quest 3, with software provided by GigXR, are used to engage in a rich dialogue around the interaction value of AR for radiological tasks. Reflexive thematic analysis was employed to analyse the resulting data alongside the NASA Task Load Index. Five themes were generated through the analysis and are supported by the NASA-TLX score. They demonstrate the difficulties participants had manipulating objects and the interactions requirements future systems should strive for, while also countering prior assumptions in the literature. The themes were mapped to three design considerations for future radiological AR applications that stand as the contribution of this work. These are embodied in the speculation of an AR-First world that illustrates key requirements.

Abstract: While the value of XR has been broadly demonstrated in the literature, there has been little empirical work seeking to establish where AR could effectively be placed within the training pathway. This is particularly true in radiology, where training relies heavily on anatomical comprehension and AR has been proposed as a tool to support this through enhanced 3D visualisation of complex anatomical structures. A series of seven interviews with radiologists and radiology registrars was conducted, using AR anatomy tasks with the Microsoft HoloLens 2 and Meta Quest 3 to engage participants in a rich dialogue around the educational value of AR in radiological training. Reflexive thematic analysis was employed to interpret the resulting data. Four themes were generated, revealing an expertise reversal effect whereby AR’s value is greatest for early-stage trainees and diminishes with experience, alongside a need for AR to integrate with, rather than replace, the traditional 2D methods used in practice. These themes were mapped to two design considerations for AR-enabled radiological curricula that stand as the contribution of this work; prioritising early-stage training through a structured graduation pathway into traditional practice, and positioning AR as a supplement to existing training rather than a replacement for it.

Abstract: Open Educational Resources (OER) are teaching, learning, and research materials that are freely accessible and openly licensed, allowing users to use, adapt, and redistribute them with few or no restrictions. This article presents a quality evaluation experience over a set of OERs, a prior step to a clusterization process based on specific criteria. The evaluators have been students, from a teacher training master's program, that were instructed in concepts related to Open Learning, digital educational repositories, design and production processes for digital educational materials, and OER quality standards. The experiment consisted of evaluating the OERs stored in an online repository called Procomun, resources associated with the discipline of Computer Science. The resources have been created by both professionals and the students themselves, with the aim of comparing production quality levels and various specific criteria between them. For this purpose, two types of evaluations were carried out. First, the quality of the repository’s semantic tagging, based on the Learning Object Metadata (LOM) standard, was assessed using the Metadata Quality Assessment Model. Second, the UNE 71362 standard was applied to a selected collection of OERs obtaining a set of spider diagrams. Finally, to evaluate the value of the quality assessment itself, two types of processes were carried out: students acted as evaluators of the resources they had produced themselves (as a self-assessment task), and peer assessment was also carried out by other students. The article describes the entire experience, the evaluation process, the quality framework and the results obtained in the experimentation.

Abstract: Although pairwise comparison-based MCDA methodologies such as AHP, ANP, and Fuzzy AHP have matured analytically, their adoption in group decision-making remains limited by a software gap: there is no open-source platform that integrates all three within a single architecture, and there is a lack of real-time multi-expert collaboration on the aggregation of pairwise comparison matrices. This paper introduces ThinkDecision, an open-source web platform that integrates client- side computation engines for all three methodologies (O(n²) for AHP/Fuzzy AHP, O(k · N³) forANP) with WebSocket synchronization supporting multi-expert AIJ/AIP aggregation at a latency of <50 ms. Validation shows a maximum deviation of 0.41–0.94% and machine precision (∼ 10⁻¹⁶) for aggregation, while latency remains below 100 ms. A case study on ERP vendor selection revealed rank reversal phenomena and a heterogeneity threshold (D_L1 ≈ 0.20) above which AIJ and AIP diverge, demonstrating that the choice of methodology and aggregation strategy can materially alter decision outcomes and inconsistencies detectable only through multi-methodological evaluations such as ThinkDecision.

Abstract: GLAM (Galleries, Libraries, Archives, and Museums) institutions have been making digital collections available for decades. New initiatives to publish, preserve, and reuse data have emerged, with collaborative projects such as Wikidata playing a significant role. This work provides a framework for extracting multimodal collections as data, using Wikidata as the primary source, along with a selection of research scenarios for reusing this data in line with emergent trends in data publication and reuse. Results showed that current trends in data preservation can be achieved through open-source code, cloud services, and collaborative platforms. Future work to be explored includes adopting best practices for provenance documentation and refining reuse scenarios.

Abstract: In an age of pervasive connectivity and rapidly evolving cyber threats, cybersecurity has become a transversal competency in engineering education rather than a niche specialism. This paper presents MoonPhase, a multifunctional portable device based on a Raspberry Pi platform, designed to support experiential cybersecurity learning in engineering education contexts by integrating offensive, defensive and educational modes into a single physical artefact. The work builds on a PRISMA-based systematic literature review and a state-of-the-art analysis of portable cybersecurity tools to derive design requirements that balance realism, safety and pedagogical alignment. MoonPhase combines sub-GHz replay capabilities, 2.4 GHz interference and monitoring, fake Wi-Fi access points, packet sniffing and network scanning in a compact, menu-driven platform that can be deployed in regular laboratories and outreach workshops. The paper describes the hardware and software architecture of the device and, more importantly, its instructional framing, outlining learning outcomes, example lab sessions and assessment strategies focused on cybersecurity literacy, systems thinking and ethical awareness. This study reports on the design and implementation of MoonPhase and presents a detailed conceptual and evaluation framework; empirical evidence from classroom deployments will be addressed in subsequent work. The device is positioned as a replicable open educational resource that brings students closer to realistic attack and defence scenarios in controlled settings.

Abstract: This article presents the design and deployment of ClimaBogotá v1.2, a climate prediction system tailored for high-altitude urban micro-zones in Bogotá, Colombia. The system combines low-cost IoT sensing, machine learning modeling, and cloud-based orchestration to enable scalable and affordable meteorological forecasting. Its architecture comprises Raspberry Pi-based weather stations, a Random Forest model trained on engineered temporal features, and an n8n-driven automation pipeline for real-time inference and dissemination via Telegram, PostgreSQL, and Grafana. With a Mean Absolute Error of 2.59°C and an R² of 0.6286 on a 30-minute forecast horizon, the system demonstrates both predictive reliability and operational feasibility using free-tier cloud resources. Unlike traditional weather systems, ClimaBogotá emphasizes modularity, adaptability, and cost-efficiency, offering a replicable framework for decentralized climate monitoring in data-scarce urban environments. Temporal misalignment between sensor nodes was identified as the primary constraint, informing future enhancements toward distributed learning strategies.

Abstract: This study introduces a novel physical layer authentication technique for Internet of Things (IoT) networks, leveraging Channel State Information (CSI) data from Wi-Fi signals to distinguish between authorized and unauthorized nodes, thereby enhancing security without compromising performance. Its novelty lies in the integrated framework that employs Non-negative Matrix Factorization (NMF) for efficient feature selection and a Gaussian Mixture Model (GMM) to identify complex patterns within the CSI data, adapting to the dynamic nature of IoT networks. The model demonstrates exceptional classification proficiency, achieving an accuracy rate of 99.83% and a recall of 100%, which is important for critical applications such as cybersecurity and anomaly detection, where identifying threats is of key importance. Furthermore, the F1-score of 99.84% reflects a strong balance between precision and recall. From a practical standpoint, the system is designed for efficiency and minimal resource consumption, exhibiting good computational efficiency, reduced training duration, and lower energy consumption compared to more complex architectures such as CNN and CNN+LSTM. This balance of high performance and resource efficiency makes it particularly suitable for deployment in resource-constrained IoT environments.

Abstract: In this study, we introduce idempotent Neutrosophic Hypersoft Rough Fuzzy Matrices (INHSRFMs) and focus on a particular case, the INHSRFM of T-type. We derive various properties for both INHSRFM and INHSRFM of T-type and present a series of theorems that validate our results. To illustrate the application of these theorems, a numerical example is included. Additionally, we propose an algorithm designed to solve decision-making (DM) problems using NHSRFMs. The practical applicability of the proposed method is demonstrated through an example.

Abstract: In real-world decision-making, constructing mathematical models is often difficult because the data are incomplete, uncertain, or even contradictory. The neutrosophic refined set provides a robust and flexible approach for effectively handling and representing these types of uncertainties. Various studies have highlighted its significant applications in decision making. In this study, a power mean operator is introduced to aggregate multiple Neutrosophic Refined Sets (NRSs) into a Single-Valued Neutrosophic Set (SVNs). The core mathematical properties of the newly introduced neutrosophic refined power mean operator are established. Moreover, two categories of neutrosophic refined cross-entropy measures are presented: one adapted from the SVNs-cross-entropy measure, and the other specifically formulated for neutrosophic refined sets. By employing the defined measures, an innovative decision making strategy is developed under the neutrosophic refined set environment. To demonstrate the effectiveness and practical relevance of the grounded strategy a numerical example based on the selection of an educational stream is solved.

Abstract: Software as a Service (SaaS) has become a key enabler of digital transformation and e-government modernization through scalable, flexible, and cost-effective service delivery. However, evidence of SaaS adoption in Southern African Development Community (SADC) public sectors remains limited and uneven. This study systematically reviews literature published between 2015 and 2025 on SaaS adoption, digital readiness, infrastructure, policy environments, and institutional capacity across SADC member states. Using PRISMA-guided screening, 31 studies were synthesized through narrative thematic analysis informed by the Technology–Organisation–Environment (TOE) framework and Institutional Theory. The findings reveal significant disparities in SaaS readiness across the region. South Africa is the only country with substantial empirical evidence of public-sector SaaS adoption, while most member states demonstrate only indirect indicators of readiness, including ICT maturity and e-government development. Four major barriers were identified: infrastructure deficits, policy and regulatory fragmentation, institutional capacity constraints, and uneven regional readiness. The study also identifies a “readiness paradox,” whereby stricter data sovereignty regulations co-exist with inadequate infrastructure for compliant SaaS deployment. The study contributes a contextualized framework for sustainable SaaS adoption in SADC public sectors.